Overview

Dataset statistics

Number of variables15
Number of observations3299
Missing cells0
Missing cells (%)0.0%
Duplicate rows21
Duplicate rows (%)0.6%
Total size in memory367.3 KiB
Average record size in memory114.0 B

Variable types

Boolean2
Categorical5
Numeric8

Alerts

Transported has constant value ""Constant
Dataset has 21 (0.6%) duplicate rowsDuplicates
Cabin_deck is highly overall correlated with Consumption_High_End and 1 other fieldsHigh correlation
Consumption_Basic is highly overall correlated with FoodCourtHigh correlation
Consumption_High_End is highly overall correlated with Cabin_deckHigh correlation
FoodCourt is highly overall correlated with Consumption_BasicHigh correlation
HomePlanet is highly overall correlated with Cabin_deckHigh correlation
CryoSleep is highly imbalanced (90.7%)Imbalance
VIP is highly imbalanced (79.2%)Imbalance
RoomService has 1222 (37.0%) zerosZeros
FoodCourt has 1498 (45.4%) zerosZeros
ShoppingMall has 1499 (45.4%) zerosZeros
Spa has 1114 (33.8%) zerosZeros
VRDeck has 1234 (37.4%) zerosZeros
Consumption_High_End has 112 (3.4%) zerosZeros
Consumption_Basic has 572 (17.3%) zerosZeros

Reproduction

Analysis started2024-05-07 10:50:28.896071
Analysis finished2024-05-07 10:50:43.786237
Duration14.89 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CryoSleep
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
False
3260 
True
 
39
ValueCountFrequency (%)
False 3260
98.8%
True 39
 
1.2%
2024-05-07T12:50:43.890046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
TRAPPIST-1e
2521 
55 Cancri e
548 
PSO J318.5-22
 
230

Length

Max length13
Median length11
Mean length11.139436
Min length11

Characters and Unicode

Total characters36749
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowTRAPPIST-1e
4th row55 Cancri e
5th row55 Cancri e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 2521
76.4%
55 Cancri e 548
 
16.6%
PSO J318.5-22 230
 
7.0%

Length

2024-05-07T12:50:44.089578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:50:44.266941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 2521
54.5%
55 548
 
11.8%
cancri 548
 
11.8%
e 548
 
11.8%
pso 230
 
5.0%
j318.5-22 230
 
5.0%

Most occurring characters

ValueCountFrequency (%)
P 5272
14.3%
T 5042
13.7%
e 3069
8.4%
S 2751
7.5%
- 2751
7.5%
1 2751
7.5%
A 2521
6.9%
I 2521
6.9%
R 2521
6.9%
5 1326
 
3.6%
Other values (13) 6224
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 5272
14.3%
T 5042
13.7%
e 3069
8.4%
S 2751
7.5%
- 2751
7.5%
1 2751
7.5%
A 2521
6.9%
I 2521
6.9%
R 2521
6.9%
5 1326
 
3.6%
Other values (13) 6224
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 5272
14.3%
T 5042
13.7%
e 3069
8.4%
S 2751
7.5%
- 2751
7.5%
1 2751
7.5%
A 2521
6.9%
I 2521
6.9%
R 2521
6.9%
5 1326
 
3.6%
Other values (13) 6224
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 5272
14.3%
T 5042
13.7%
e 3069
8.4%
S 2751
7.5%
- 2751
7.5%
1 2751
7.5%
A 2521
6.9%
I 2521
6.9%
R 2521
6.9%
5 1326
 
3.6%
Other values (13) 6224
16.9%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
False
3191 
True
 
108
ValueCountFrequency (%)
False 3191
96.7%
True 108
 
3.3%
2024-05-07T12:50:44.446203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

ZEROS 

Distinct1187
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.50995
Minimum0
Maximum14327
Zeros1222
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:44.678468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49
Q3690
95-th percentile2058.8
Maximum14327
Range14327
Interquartile range (IQR)690

Descriptive statistics

Standard deviation939.37342
Coefficient of variation (CV)1.9034539
Kurtosis33.046845
Mean493.50995
Median Absolute Deviation (MAD)49
Skewness4.4329406
Sum1628089.3
Variance882422.43
MonotonicityNot monotonic
2024-05-07T12:50:44.927957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1222
37.0%
1 67
 
2.0%
2 38
 
1.2%
3 31
 
0.9%
4 27
 
0.8%
6 15
 
0.5%
9 14
 
0.4%
5 13
 
0.4%
13 11
 
0.3%
19 10
 
0.3%
Other values (1177) 1851
56.1%
ValueCountFrequency (%)
0 1222
37.0%
1 67
 
2.0%
2 38
 
1.2%
3 31
 
0.9%
4 27
 
0.8%
5 13
 
0.4%
6 15
 
0.5%
7 7
 
0.2%
8 5
 
0.2%
9 14
 
0.4%
ValueCountFrequency (%)
14327 1
< 0.1%
9920 1
< 0.1%
8586 1
< 0.1%
8243 1
< 0.1%
8209 1
< 0.1%
8168 1
< 0.1%
8142 1
< 0.1%
8030 1
< 0.1%
7406 1
< 0.1%
7172 1
< 0.1%

FoodCourt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct887
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340.47459
Minimum0
Maximum13561
Zeros1498
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:45.221641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3219.5
95-th percentile1855
Maximum13561
Range13561
Interquartile range (IQR)219.5

Descriptive statistics

Standard deviation948.74852
Coefficient of variation (CV)2.7865472
Kurtosis45.513316
Mean340.47459
Median Absolute Deviation (MAD)3
Skewness5.6459835
Sum1123225.7
Variance900123.75
MonotonicityNot monotonic
2024-05-07T12:50:45.502609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1498
45.4%
1 88
 
2.7%
2 57
 
1.7%
4 41
 
1.2%
3 39
 
1.2%
5 27
 
0.8%
10 23
 
0.7%
6 21
 
0.6%
7 21
 
0.6%
9 19
 
0.6%
Other values (877) 1465
44.4%
ValueCountFrequency (%)
0 1498
45.4%
1 88
 
2.7%
2 57
 
1.7%
3 39
 
1.2%
4 41
 
1.2%
5 27
 
0.8%
6 21
 
0.6%
7 21
 
0.6%
8 14
 
0.4%
9 19
 
0.6%
ValueCountFrequency (%)
13561 1
< 0.1%
12563 1
< 0.1%
11441 1
< 0.1%
9965 1
< 0.1%
8759 1
< 0.1%
8403 1
< 0.1%
8151 1
< 0.1%
8150 1
< 0.1%
7964 1
< 0.1%
7452 1
< 0.1%

ShoppingMall
Real number (ℝ)

ZEROS 

Distinct740
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.4451
Minimum0
Maximum4940
Zeros1499
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:45.761739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3132.5
95-th percentile834.1
Maximum4940
Range4940
Interquartile range (IQR)132.5

Descriptive statistics

Standard deviation381.55627
Coefficient of variation (CV)2.3633809
Kurtosis34.236838
Mean161.4451
Median Absolute Deviation (MAD)2
Skewness4.7378629
Sum532607.39
Variance145585.19
MonotonicityNot monotonic
2024-05-07T12:50:46.467134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1499
45.4%
1 111
 
3.4%
2 56
 
1.7%
3 39
 
1.2%
4 35
 
1.1%
6 27
 
0.8%
7 25
 
0.8%
10 24
 
0.7%
5 23
 
0.7%
8 22
 
0.7%
Other values (730) 1438
43.6%
ValueCountFrequency (%)
0 1499
45.4%
1 111
 
3.4%
2 56
 
1.7%
3 39
 
1.2%
4 35
 
1.1%
5 23
 
0.7%
6 27
 
0.8%
7 25
 
0.8%
8 22
 
0.7%
9 17
 
0.5%
ValueCountFrequency (%)
4940 1
< 0.1%
4790 1
< 0.1%
4447 1
< 0.1%
4058 1
< 0.1%
3415 1
< 0.1%
2975 1
< 0.1%
2974 1
< 0.1%
2929 1
< 0.1%
2915 1
< 0.1%
2778 1
< 0.1%

Spa
Real number (ℝ)

ZEROS 

Distinct1210
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean653.23535
Minimum0
Maximum16139
Zeros1114
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:46.719178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53
Q3658.5
95-th percentile3540.2
Maximum16139
Range16139
Interquartile range (IQR)658.5

Descriptive statistics

Standard deviation1488.3135
Coefficient of variation (CV)2.2783727
Kurtosis27.098163
Mean653.23535
Median Absolute Deviation (MAD)53
Skewness4.4753054
Sum2155023.4
Variance2215077.2
MonotonicityNot monotonic
2024-05-07T12:50:47.028447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1114
33.8%
1 85
 
2.6%
2 49
 
1.5%
4 25
 
0.8%
5 22
 
0.7%
3 21
 
0.6%
9 19
 
0.6%
6 17
 
0.5%
7 16
 
0.5%
8 16
 
0.5%
Other values (1200) 1915
58.0%
ValueCountFrequency (%)
0 1114
33.8%
1 85
 
2.6%
2 49
 
1.5%
3 21
 
0.6%
4 25
 
0.8%
5 22
 
0.7%
6 17
 
0.5%
7 16
 
0.5%
8 16
 
0.5%
9 19
 
0.6%
ValueCountFrequency (%)
16139 1
< 0.1%
15586 1
< 0.1%
15331 1
< 0.1%
15238 1
< 0.1%
13995 1
< 0.1%
13104 1
< 0.1%
12062 1
< 0.1%
11001 1
< 0.1%
10976 1
< 0.1%
10941 1
< 0.1%

VRDeck
Real number (ℝ)

ZEROS 

Distinct1185
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646.04047
Minimum0
Maximum24133
Zeros1234
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:47.283439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27
Q3607.5
95-th percentile3408.3
Maximum24133
Range24133
Interquartile range (IQR)607.5

Descriptive statistics

Standard deviation1611.5102
Coefficient of variation (CV)2.4944415
Kurtosis40.677037
Mean646.04047
Median Absolute Deviation (MAD)27
Skewness5.2783244
Sum2131287.5
Variance2596965.1
MonotonicityNot monotonic
2024-05-07T12:50:47.575836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1234
37.4%
1 81
 
2.5%
2 35
 
1.1%
3 31
 
0.9%
5 29
 
0.9%
4 24
 
0.7%
6 15
 
0.5%
7 15
 
0.5%
16 13
 
0.4%
8 13
 
0.4%
Other values (1175) 1809
54.8%
ValueCountFrequency (%)
0 1234
37.4%
1 81
 
2.5%
2 35
 
1.1%
3 31
 
0.9%
4 24
 
0.7%
5 29
 
0.9%
6 15
 
0.5%
7 15
 
0.5%
8 13
 
0.4%
9 12
 
0.4%
ValueCountFrequency (%)
24133 1
< 0.1%
20336 1
< 0.1%
17074 1
< 0.1%
16337 1
< 0.1%
12708 1
< 0.1%
12682 1
< 0.1%
12424 1
< 0.1%
12392 1
< 0.1%
12323 1
< 0.1%
12143 1
< 0.1%

Cabin_deck
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
F
1429 
G
632 
E
481 
D
258 
C
218 
Other values (3)
281 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3299
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowG
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 1429
43.3%
G 632
19.2%
E 481
 
14.6%
D 258
 
7.8%
C 218
 
6.6%
B 170
 
5.2%
A 107
 
3.2%
T 4
 
0.1%

Length

2024-05-07T12:50:47.795146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:50:47.987787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 1429
43.3%
g 632
19.2%
e 481
 
14.6%
d 258
 
7.8%
c 218
 
6.6%
b 170
 
5.2%
a 107
 
3.2%
t 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 1429
43.3%
G 632
19.2%
E 481
 
14.6%
D 258
 
7.8%
C 218
 
6.6%
B 170
 
5.2%
A 107
 
3.2%
T 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 1429
43.3%
G 632
19.2%
E 481
 
14.6%
D 258
 
7.8%
C 218
 
6.6%
B 170
 
5.2%
A 107
 
3.2%
T 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 1429
43.3%
G 632
19.2%
E 481
 
14.6%
D 258
 
7.8%
C 218
 
6.6%
B 170
 
5.2%
A 107
 
3.2%
T 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 1429
43.3%
G 632
19.2%
E 481
 
14.6%
D 258
 
7.8%
C 218
 
6.6%
B 170
 
5.2%
A 107
 
3.2%
T 4
 
0.1%

Group_size
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8490452
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:48.208131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.1
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5111095
Coefficient of variation (CV)0.81723775
Kurtosis5.2952
Mean1.8490452
Median Absolute Deviation (MAD)0
Skewness2.3304131
Sum6100
Variance2.2834519
MonotonicityNot monotonic
2024-05-07T12:50:48.407892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 2038
61.8%
2 626
 
19.0%
3 292
 
8.9%
4 114
 
3.5%
7 76
 
2.3%
5 64
 
1.9%
8 46
 
1.4%
6 43
 
1.3%
ValueCountFrequency (%)
1 2038
61.8%
2 626
 
19.0%
3 292
 
8.9%
4 114
 
3.5%
5 64
 
1.9%
6 43
 
1.3%
7 76
 
2.3%
8 46
 
1.4%
ValueCountFrequency (%)
8 46
 
1.4%
7 76
 
2.3%
6 43
 
1.3%
5 64
 
1.9%
4 114
 
3.5%
3 292
 
8.9%
2 626
 
19.0%
1 2038
61.8%

HomePlanet
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
Earth
1901 
Mars
770 
Europa
628 

Length

Max length6
Median length5
Mean length4.9569567
Min length4

Characters and Unicode

Total characters16353
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEuropa
3rd rowEarth
4th rowMars
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 1901
57.6%
Mars 770
23.3%
Europa 628
 
19.0%

Length

2024-05-07T12:50:48.645115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:50:48.860291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 1901
57.6%
mars 770
23.3%
europa 628
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 3299
20.2%
r 3299
20.2%
E 2529
15.5%
t 1901
11.6%
h 1901
11.6%
M 770
 
4.7%
s 770
 
4.7%
u 628
 
3.8%
o 628
 
3.8%
p 628
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3299
20.2%
r 3299
20.2%
E 2529
15.5%
t 1901
11.6%
h 1901
11.6%
M 770
 
4.7%
s 770
 
4.7%
u 628
 
3.8%
o 628
 
3.8%
p 628
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3299
20.2%
r 3299
20.2%
E 2529
15.5%
t 1901
11.6%
h 1901
11.6%
M 770
 
4.7%
s 770
 
4.7%
u 628
 
3.8%
o 628
 
3.8%
p 628
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3299
20.2%
r 3299
20.2%
E 2529
15.5%
t 1901
11.6%
h 1901
11.6%
M 770
 
4.7%
s 770
 
4.7%
u 628
 
3.8%
o 628
 
3.8%
p 628
 
3.8%

Transported
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
0
3299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3299
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3299
100.0%

Length

2024-05-07T12:50:49.077335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:50:49.253737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3299
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3299
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3299
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3299
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3299
100.0%

Consumption_High_End
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2073
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1792.7858
Minimum0
Maximum25463.229
Zeros112
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:49.474908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153
Q1636.93884
median916
Q31998
95-th percentile6341.1
Maximum25463.229
Range25463.229
Interquartile range (IQR)1361.0612

Descriptive statistics

Standard deviation2253.6199
Coefficient of variation (CV)1.2570492
Kurtosis14.430452
Mean1792.7858
Median Absolute Deviation (MAD)463
Skewness3.1937766
Sum5914400.2
Variance5078802.6
MonotonicityNot monotonic
2024-05-07T12:50:49.800843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
3.4%
804 14
 
0.4%
809 10
 
0.3%
788 8
 
0.2%
771 7
 
0.2%
791 7
 
0.2%
625 7
 
0.2%
738 7
 
0.2%
759 7
 
0.2%
754 7
 
0.2%
Other values (2063) 3113
94.4%
ValueCountFrequency (%)
0 112
3.4%
13 1
 
< 0.1%
14 1
 
< 0.1%
20 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
38 1
 
< 0.1%
38.26729563 1
 
< 0.1%
44 1
 
< 0.1%
45 3
 
0.1%
ValueCountFrequency (%)
25463.22895 1
< 0.1%
20961 1
< 0.1%
18037 1
< 0.1%
17928 1
< 0.1%
16826 1
< 0.1%
16762 1
< 0.1%
16394 1
< 0.1%
16059 1
< 0.1%
15758 1
< 0.1%
14695 1
< 0.1%

Consumption_Basic
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1249
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.91969
Minimum0
Maximum13576
Zeros572
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2024-05-07T12:50:50.064475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median137
Q3584
95-th percentile2243.8
Maximum13576
Range13576
Interquartile range (IQR)578

Descriptive statistics

Standard deviation996.45028
Coefficient of variation (CV)1.9852783
Kurtosis35.273753
Mean501.91969
Median Absolute Deviation (MAD)137
Skewness4.8063564
Sum1655833.1
Variance992913.16
MonotonicityNot monotonic
2024-05-07T12:50:50.380380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 572
 
17.3%
1 82
 
2.5%
2 48
 
1.5%
3 36
 
1.1%
4 35
 
1.1%
5 31
 
0.9%
10 29
 
0.9%
6 26
 
0.8%
13 26
 
0.8%
7 23
 
0.7%
Other values (1239) 2391
72.5%
ValueCountFrequency (%)
0 572
17.3%
1 82
 
2.5%
2 48
 
1.5%
3 36
 
1.1%
4 35
 
1.1%
5 31
 
0.9%
6 26
 
0.8%
7 23
 
0.7%
8 15
 
0.5%
9 21
 
0.6%
ValueCountFrequency (%)
13576 1
< 0.1%
12563 1
< 0.1%
11441 1
< 0.1%
9965 1
< 0.1%
8759 1
< 0.1%
8403 1
< 0.1%
8156 1
< 0.1%
8150 1
< 0.1%
7964 1
< 0.1%
7452 1
< 0.1%

Age_group
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
Young adults
2248 
Middle-aged
640 
Minor
297 
Senior
 
114

Length

Max length12
Median length12
Mean length10.968475
Min length5

Characters and Unicode

Total characters36185
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiddle-aged
2nd rowYoung adults
3rd rowMiddle-aged
4th rowYoung adults
5th rowYoung adults

Common Values

ValueCountFrequency (%)
Young adults 2248
68.1%
Middle-aged 640
 
19.4%
Minor 297
 
9.0%
Senior 114
 
3.5%

Length

2024-05-07T12:50:50.685762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:50:50.903603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
young 2248
40.5%
adults 2248
40.5%
middle-aged 640
 
11.5%
minor 297
 
5.4%
senior 114
 
2.1%

Most occurring characters

ValueCountFrequency (%)
u 4496
12.4%
d 4168
11.5%
l 2888
8.0%
g 2888
8.0%
a 2888
8.0%
n 2659
 
7.3%
o 2659
 
7.3%
t 2248
 
6.2%
s 2248
 
6.2%
Y 2248
 
6.2%
Other values (7) 6795
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 4496
12.4%
d 4168
11.5%
l 2888
8.0%
g 2888
8.0%
a 2888
8.0%
n 2659
 
7.3%
o 2659
 
7.3%
t 2248
 
6.2%
s 2248
 
6.2%
Y 2248
 
6.2%
Other values (7) 6795
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 4496
12.4%
d 4168
11.5%
l 2888
8.0%
g 2888
8.0%
a 2888
8.0%
n 2659
 
7.3%
o 2659
 
7.3%
t 2248
 
6.2%
s 2248
 
6.2%
Y 2248
 
6.2%
Other values (7) 6795
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 4496
12.4%
d 4168
11.5%
l 2888
8.0%
g 2888
8.0%
a 2888
8.0%
n 2659
 
7.3%
o 2659
 
7.3%
t 2248
 
6.2%
s 2248
 
6.2%
Y 2248
 
6.2%
Other values (7) 6795
18.8%

Interactions

2024-05-07T12:50:41.602327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:30.380790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:32.165909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:33.700404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:35.140481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:36.641952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:38.142825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:39.887987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:41.769336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:30.576242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:32.360071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:33.941738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:35.326521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:36.836193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:38.352897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:40.104549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:41.967793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:30.811577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:32.549848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.131543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:35.511955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.003076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:38.575318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:40.342306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:42.140843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:31.050418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:32.719845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.336828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:35.712703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.168272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:38.767552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:40.537091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:42.324365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:31.286090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:32.932086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.521032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:35.895099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.366248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:38.980755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:40.754118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:42.481390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:31.487164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:33.101962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.674860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:36.080606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.553657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:39.244026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:40.949728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:42.702720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:31.705440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:33.301838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.826412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:36.260733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.764947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:39.474752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:41.168593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:42.883823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:31.931108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:33.485044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:34.988614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:36.442044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:37.946342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:39.660455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:50:41.404968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-07T12:50:51.080257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Age_groupCabin_deckConsumption_BasicConsumption_High_EndCryoSleepDestinationFoodCourtGroup_sizeHomePlanetRoomServiceShoppingMallSpaVIPVRDeck
Age_group1.0000.097-0.054-0.0440.1510.000-0.040-0.0550.1200.002-0.021-0.0110.062-0.016
Cabin_deck0.0971.000-0.351-0.5220.1480.223-0.347-0.1910.7210.0180.023-0.2560.226-0.259
Consumption_Basic-0.054-0.3511.0000.1440.0000.1140.6390.0850.343-0.0280.4700.1570.1190.166
Consumption_High_End-0.044-0.5220.1441.0000.0110.1810.2320.1680.4980.164-0.1280.3880.1310.352
CryoSleep0.1510.1480.0000.0111.0000.049-0.1090.1620.061-0.122-0.109-0.1280.000-0.122
Destination0.0000.2230.1140.1810.0491.000-0.166-0.0260.2350.0650.077-0.0910.072-0.166
FoodCourt-0.040-0.3470.6390.232-0.109-0.1661.0000.0960.358-0.231-0.1820.2590.1260.329
Group_size-0.055-0.1910.0850.1680.162-0.0260.0961.0000.263-0.042-0.0470.0600.0950.051
HomePlanet0.1200.7210.3430.4980.0610.2350.3580.2631.0000.3340.2520.0520.218-0.077
RoomService0.0020.018-0.0280.164-0.1220.065-0.231-0.0420.3341.0000.245-0.3100.054-0.382
ShoppingMall-0.0210.0230.470-0.128-0.1090.077-0.182-0.0470.2520.2451.000-0.1250.045-0.187
Spa-0.011-0.2560.1570.388-0.128-0.0910.2590.0600.052-0.310-0.1251.0000.0530.039
VIP0.0620.2260.1190.1310.0000.0720.1260.0950.2180.0540.0450.0531.0000.062
VRDeck-0.016-0.2590.1660.352-0.122-0.1660.3290.051-0.077-0.382-0.1870.0390.0621.000

Missing values

2024-05-07T12:50:43.212051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T12:50:43.633243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
2FalseTRAPPIST-1eTrue43.03576.00.0000006715.049.0A2Europa06807.03576.000000Middle-aged
3FalseTRAPPIST-1eFalse0.01283.0371.0000003329.0193.0A2Europa03522.01654.000000Young adults
13FalseTRAPPIST-1eFalse719.01.065.0000000.024.0G1Earth0743.066.000000Middle-aged
16False55 Cancri eFalse1286.0122.0617.1497950.00.0F1Mars01286.0739.149795Young adults
17False55 Cancri eFalse0.01.00.0000000.0637.0F1Earth0637.01.000000Young adults
20False55 Cancri eFalse412.00.01.0000000.0679.0F2Earth01091.01.000000Minor
21TrueTRAPPIST-1eFalse0.00.00.0000000.00.0E6Earth00.00.000000Minor
27FalseTRAPPIST-1eFalse980.02.069.0000000.00.0D1Mars0980.071.000000Young adults
29FalseTRAPPIST-1eFalse0.0225.00.000000998.00.0F1Earth0998.0225.000000Minor
31FalseTRAPPIST-1eFalse1125.00.0136.00000048.00.0F1Mars01173.0136.000000Middle-aged
CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
8605FalseTRAPPIST-1eFalse2.045.045.00.0815.0G3Earth0817.090.0Young adults
8610FalseTRAPPIST-1eFalse745.0639.0977.04.00.0F1Mars0749.01616.0Young adults
8611FalseTRAPPIST-1eFalse1.0197.00.0660.015.0F1Earth0676.0197.0Middle-aged
8612FalseTRAPPIST-1eFalse676.00.013.00.012.0F1Earth0688.013.0Young adults
8621FalseTRAPPIST-1eFalse0.00.00.00.00.0E3Mars00.00.0Young adults
8623FalseTRAPPIST-1eFalse6.01.0638.01107.023.0G1Earth01136.0639.0Middle-aged
8626FalseTRAPPIST-1eFalse2.00.0918.0128.00.0E1Mars0130.0918.0Minor
8627FalseTRAPPIST-1eFalse699.00.0600.00.00.0E1Mars0699.0600.0Young adults
8649FalseTRAPPIST-1eFalse86.03.0149.0208.0329.0F2Earth0623.0152.0Young adults
8657False55 Cancri eFalse0.01049.00.0353.03235.0E2Europa03588.01049.0Young adults

Duplicate rows

Most frequently occurring

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group# duplicates
8FalseTRAPPIST-1eFalse0.00.00.00.00.0G7Earth00.00.0Minor13
0FalseTRAPPIST-1eFalse0.00.00.00.00.0E1Mars00.00.0Young adults10
19TrueTRAPPIST-1eFalse0.00.00.00.00.0G7Earth00.00.0Minor10
1FalseTRAPPIST-1eFalse0.00.00.00.00.0E2Mars00.00.0Young adults9
7FalseTRAPPIST-1eFalse0.00.00.00.00.0G6Earth00.00.0Minor9
4FalseTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Young adults8
6FalseTRAPPIST-1eFalse0.00.00.00.00.0G5Earth00.00.0Minor5
15TrueTRAPPIST-1eFalse0.00.00.00.00.0E2Mars00.00.0Young adults5
16TrueTRAPPIST-1eFalse0.00.00.00.00.0E3Mars00.00.0Young adults5
20TrueTRAPPIST-1eFalse0.00.00.00.00.0G8Earth00.00.0Minor5